Zhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752
| Reference Type | Journal (article/letter/editorial) | ||
|---|---|---|---|
| Title | Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling | ||
| Journal | Computational Materials Science | ||
| Authors | Zhu, Siya | Author | |
| Arróyave, Raymundo | Author | ||
| Year | 2026 (June) | Volume | 270 |
| Publisher | Elsevier BV | ||
| DOI | doi:10.1016/j.commatsci.2026.114752Search in ResearchGate | ||
| Generate Citation Formats | |||
| Mindat Ref. ID | 19933364 | Long-form Identifier | mindat:1:5:19933364:8 |
| GUID | 0 | ||
| Full Reference | Zhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752 | ||
| Plain Text | Zhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752 | ||
| In | (2026) Computational Materials Science Vol. 270. Elsevier BV | ||
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